1 EVIDENCE-BASED POLICY AND POLITICAL CONTROL: WHAT DOES REGULATORY IMPACT ASSESSMENT TELL US?
Bibliographic record
Abstract
Instead of examining how research produced outside government is used (or not used, or used in unintended ways), this paper considers a policy instrument in which analysis is carried out by civil servants. This is regulatory impact assessment, i.e., the process through which regulators appraise the costs and benefits of proposed regulations and how proposals affect different stakeholders. Since public officers are the analysts, the ways in which evidence is used, edited, abused, and translated into policy are determined by political relationships between the bureaucracy and its political master. Consequently, instead of looking exclusively at instrumental usages of evidence or lack of (typically by measuring if decisions are sensitive to empirics), we borrow from the positive political economy of delegation the hypothesis that evidence is an important component of control procedures. Rational politicians – positive political economy goes – design administrative processes based on impact assessment to solve problems of political uncertainty and maximise power over the long-term. This is a rather abstract hypothesis, but with clearly observable implications. The paper is an effort to present empirical analysis on Canada, Denmark,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.193 | 0.457 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.003 | 0.048 |
| Scholarly communication | 0.033 | 0.035 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".